Author: Wenwu Shi, Sr research engineer

SYNOPSIS

Monitoring and improving melt quality could facilitate the production of high quality castings. This article introduces ‘Vmet’ (Vesuvius metal quality analysis) based on the microstructural analysis using a scanning electron microscope. By using the pre-defined selection rules and image processing algorithms, size, morphology, and composition of defects (pores and oxides) are recorded. Total counts and area fraction of defects in two sets of samples collected before and after degassing were analysed. Results show that degassing can effectively reduce the size, amount, and aspect ratio of defects. Statistical analysis based on the extreme value theory was applied to analyse the data. This technique can also detect bi-films and dendritic pores using spatial distribution of individual defects. As the Vmet rules are highly customisable, analysis of TiB2 particles in aluminium composite matrix was also demonstrated. To summarise, Vmet could be useful for monitoring the metal cleanliness and measuring the effectiveness of different treatment techniques.

BACKGROUND

Aluminium castings with high integrity are essential in the automotive industry for enhancing the fuel efficiency and reducing emissions. Innovation on the geometrical design, compositions of alloys, microstructures, processing routes (low/high pressure diecasting, squeeze casting, and semi-solid casting), and continuous improvement on melt cleanliness are also critical. The overall physical properties could prominently depend on the defects/inclusions where failure/degradation could initiate and propagate. To achieve high quality castings, the melt quality such as the hydrogen level and concentration of oxide inclusions should be delicately controlled. First and foremost, the quality of incoming aluminium ingots/liquid metal should be closely monitored and the melt should be treated properly (degassing and fluxing) to achieve the desired cleanliness prior to pouring. Throughout these steps, the melt should be handled carefully to minimise the pick-up of hydrogen and the creation of oxide inclusions. These efforts are vital to ensure the final physical properties of castings(1).

With the understanding on the importance of melt quality and correct practice to obtain clean metal, there are other questions to ask:

How would the melt cleanliness be measured?

How can we determine where ‘dirty’ metal was created in the process?

How long and what kind of treatments are needed before the actual pouring?

Over the years, different techniques have been developed. The rapid and cost-effective ‘reduced pressure test’ (RPT) could estimate the hydrogen-induced porosity by measuring the density and visually examining the cross-section. K-mold is also widely used to estimate the defects by examining the fracture surface. Other techniques such as LiMCA, PodFA were also used. An ideal technique for measuring and melt cleanliness should have the capability to directly observe defects such as pores and inclusions, quantify the size distribution, measure the composition to determine where they were picked up (hydrogen pores) or created (oxide inclusions), and correlate the final physical properties with melt treatment routes and processing parameters. This article demonstrates the utilisation of the SEM microscope equipped with automatic feature analysis, and EDX detector for measuring and classifying the pores, inclusions and other secondary particles. Statistical analysis based on extreme value theory is also demonstrated.

EXPERIMENTAL PROCEDURES

During the rotary degassing trial, four K-mold samples were cast with AlSi9Mg alloy before and after rotary degassing using Foseco’s XSR rotor. The aluminium melt was held for ~one month in a crucible furnace at ~720°C between these two degassing cycles to intentionally introduce hydrogen and oxides in the melt. For the TiB2-Al samples, the titanium and boron levels were adjusted to the desired levels in the melt. All samples were sectioned using a diamond saw with continuous water cooling. Sectioned samples were mounted into 32mm sample stubs with a heat set resin and mounted samples were polished using a Struers Hexamatic auto-polisher.

RESULT AND DISCUSSION

Fundamentals of Vmet analysis

To demonstrate the capability of Vmet analysis, a demo experiment was conducted in Foseco’s lab as shown in fig.2. Four different K-mold samples were collected at different steps: the initial aluminium melt (AlSi9Mg), the melt after five-minute rotary degassing using a Foseco XSR degassing rotor, the melt left for one month, and the melt with five-minute degassing, respectively. The Vmet sampling areas are marked with the dotted rectangles.

QUANTIFICATION OF INCLUSIONS (COUNTS AND AREA FRACTION)

One of the most widely used methods to measure the cleanliness is to calculate the number and/or volume fraction of inclusions. As Vmet is a 2-D analysis, instead of volume fraction, an inclusion index based on area fraction of these defects was used. The total numbers and the inclusion index of samples at different steps is show in fig.3a.

The initial melt had 179 inclusions and the inclusion index was 6.7. After the rotary degassing treatment, both the number of inclusions and the inclusion index dropped significantly. This not only shows that rotary degassing can effectivity improve the melt cleanliness, it also demonstrates that Vmet could be a useful tool for tracking the overall melt cleanliness and effectiveness of melt treatment. When the melt was held at high temperature, the aluminium became oxidised slowly and picked up moisture from the atmosphere. The number of features and the inclusion index for sample B2 increased sharply. The degassing treatment could restore the cleanliness (sample A2). The plots of size distribution of detected inclusions are shown in fig.3b. With the exception of sample B2, most of the inclusions are less than 20µm. When held for a long time at high temperature (sample B2), this led to the formation of some large defects. After degassing, the distribution profiles shifted towards smaller diameter inclusions and the number of inclusions also decreased. Both indicate the reduction of size and population of defects.

MORPHOLOGY OF DEFECTS

Apart from the number and size of defects, the shape/morphology of inclusions are also important. A small defect with a sharp tip could be more harmful than a large round defect as the stress could concentrate at the tip of the defect. Vmet also records the SEM images of detected inclusions (fig.4a). Fig.4b shows two inclusions with different areas (45 and 73), however, the small inclusions have some sharp corners/edges. These SEM images could be further processed into images with vectors and fed into Abaqus finite element analysis software (fig.4c). By applying the same boundary conditions and initial stress, the stress concentration factors (Kt) were calculated. As demonstrated in fig.4d, despite the first inclusion being smaller, the Kt factor is much higher than the large inclusion due to larger aspect ratio.

As demonstrated in fig.4, both the shape and area of inclusions are critical so it’s essential to evaluate both. The aspect ratio against the area for each inclusion is shown in fig.5. The inset images show three different scenarios: large round defect (small aspect ratio with large area); small spikey defect (large aspect ratio with small area); and large spikey defect (large aspect ratio with large area). Undoubtedly, the large spikey defects are the most harmful inclusion.

During the first round of treatment (fig.5a), the distribution of inclusions in the aspect ratio-area chart moved towards the origin, indicating the reduction on both the aspect ratio and inclusion area. For the second round of degassing, it was observed that there were lots of inclusions with both large aspect ratio and area, which negatively impact the final mechanical strength. After degassing, both the aspect ratio and areas had decreased.

OXIDATION OF MAGNESIUM

A loss of active elements such as magnesium could be an issue for some alloys as the final chemical composition could be different and these oxides could lead to inclusions. Most techniques characterise the overall metal composition while Vmet is only measuring the composition of inclusions. This allows tracking the transfer/loss of magnesium from melt to inclusions. The magnesium levels in the detected inclusions are shown in Table 1. From samples A1 to B2, the equivalent magnesium area increased sharply due to the preferential oxidation of more active magnesium. The estimated percentage of magnesium in all inclusions also increased, indicating that magnesium is oxidised faster than aluminium. Besides magnesium mixed with alumina, magnesium oxide inclusions also increased. It’s evident that rotary degassing could remove these oxides and restore the original metal cleanliness.

STATISTICAL ANALYSIS

Ideally speaking, Vmet analysis has no limit on sampling areas. Different sections from the casting could be mounted, polished, and scanned using this technique, however, this could be time-consuming. It’s desired to quickly estimate the overall cleanliness of the casting from a limited number of samples. Meanwhile, as all inclusions were scanned, a large amount of data is generated. Statistical analysis is needed to quickly assess the level of cleanliness. Extreme value statistic has been widely used for predicting the extreme cases, such as flooding, wind speed, and earthquakes. This was also used to evaluate the cleanliness of steel, especially for the fatigue strength as it only depends on the largest inclusion(2). The cumulative distribution function for the generalised extreme value theory is:

F (x;μ,σ,ξ) = exp {-[1+ξ(x-μ)/σ)]^(-1/ξ) }

where µ is the location parameter, σ is the scale parameter, and ξ is the shape parameter. When ξ=0, it’s a type I Gumbel distribution. When ξ>0, it’s a type II Frechet distribution. When ξ<0, it’s a reversed Weibull distribution.

The extreme value plots of the samples collected at different stages are shown in fig.6. These points were further fitted with the distribution function above to plot the predicted curve. As Samples B1 and B2 are not treated, they are on the large area 1/2 side, indicating a higher probability to find inclusions with a large area. Unlike the typical straight line observed for steel inclusions 2, extreme value plots collected from aluminium alloys are curved in this study (type II, sample B1, A1, and A2). Sample B2 could be fitted using two straight lines.

With the extreme values plotted, it’s possible to use this data to extrapolate the probability of finding a larger inclusion in a larger sample area or to find out what could be the largest possible inclusion in a particular casting based on the scanned Vmet data. The fitted extreme value plots for samples B1 and A1 are shown in fig.7. The cumulative frequencies of finding an inclusion with area 1/2 equal to 60µm in a 1mm2 scanning area are 0.99251 and 0.98643, before and after degassing respectively. The largest inclusions in larger sampling areas (5cm2 and 10cm2) were also estimated. As the fatigue strength is highly dependant on the largest inclusions, it’s possible to estimate the degree of improvement on fatigue strength based on the reduction of the largest inclusion(2).

BIFILM

Previous literature has pointed out that the surface of an aluminium melt could form a native oxide film, which could be folded and entrained into the casting to form a so called ‘bifilm’(3). These films that resemble long and thin cracks could severely decrease the mechanical strength. Bi-films are much larger than the typical inclusions/pores that Vmet can handle. When long and thin films were present in the sample, Vmet wasn’t able to analyse the whole film due to limitation on the lowest magnification. Instead, lots of fragmented short segments are recorded. In this case, it’s preferable to analyse the spatial distribution (mapping) and orientation of these individual inclusions. The strings of inclusions collected in Vmet is demonstrated in fig.8. In comparison to the SEM images, it’s clear that these strings are from bi-films.

CLUSTERS

During solidification, pores can be continuously pushed at a dendritic solid-liquid boundary. As proven by previous in-situ analysis studies, these pores could also be highly dendritic(4). As Vmet is based on the polished section, instead of connected pores, it will consider these cavities as individual defects, which is far away from the reality (fig.9a). Thus, another type of feature, named ‘clusters’, based on the collective analysis of relative distance of these defects is also considered. The number of clusters and the maximum cluster size for each sample is shown in fig.9b. After the first treatment, there were no clusters observed in sample A1, indicating a successful removal of large pores. Sample B2 has 21 clusters with the maximum cluster size being 61.5µm. This was due to the pickup of moisture during long term holding. Degassing treatment was able to restore the cleanliness of the metal. A typical inclusion map with lots of clusters is shown in fig.9c. It must be noted that folded oxides (bifilm) could also be categorised as clusters in this type of report.

EVALUATE OTHER PARTICLES IN ALUMINIUM MATRIX

As the selection criteria for Vmet is based on the brightness of the image and is highly customisable, beside the dark inclusions, it’s possible to use the same technique to evaluate any other foreign materials within the matrix. Composite materials based on aluminium matrix attracted much attention as it could combine both the advantage of the aluminium alloy and added fillers. The final physical properties of composite are determined by the size, volume fraction, and distribution of foreign particles including SiC and Al2O3 within the matrix. The clusters of TiB2 particles within the pure aluminium matrix are shown in fig.10a. These TiB2 particles were formed in-situ by adding titanium and boron into the melt. As the atomic number of titanium is higher than aluminium, they appeared to be brighter (fig.10b) when observed under a back-scattered electron detector. Therefore, a different Vmet rule was defined to detect these TiB2 particles.

Dark particles (abrasive diamond from polishing) could also be excluded. Four samples were prepared and analysed. Fig.10c shows that there is a linear relationship between the added titanium level and the number of particles as well as the total area of these particles. However, when the addition of boron was non-stoichiometric, fewer TiB2 particles were formed. Fig.10d further demonstrates the increase of distribution density when the titanium and boron levels were increased. Thus, the Vmet technique could be used to evaluate the shape, size, and distribution density of any foreign particles/fillers in the matrix, which could be useful for characterising composites.

CONCLUSIONS

Monitoring metal cleanliness down to micron scale can be challenging but it is very important for optimising melt handling and treatment to ensure castings with good physical properties. This article has considered a technique called ‘Vmet’ based on the microstructural analysis of polished aluminium samples. By using a scanning electron microscope with an automated stage and an EDX detector, a large number of defects (pores and oxides inclusions) were scanned. To demonstrate the capability of this technique, two sets of samples before and after rotary degassing were analysed. This technique could measure the size, morphology, distribution, and composition of defects and is useful for tracking the effectiveness of melt treatment. Statistical analysis based on extreme value theory was also demonstrated to predict the probability of finding larger and more harmful inclusions within a larger sample. Although it’s based on the analysis of individual particles, other defects such as bifilm and dendritic pores or clusters could be investigated by mapping the location of detected inclusions. This technique could also be used to analyse the distribution of secondary particles/fillers in the composites with aluminium matrix.

Murakami Y. ‘Inclusion rating by statistics of extreme values and its application to fatigue strength prediction and quality control of materials.’ Journal of Research-National Institute of Standards and Technology 99 (1994): 345-345.